Time length is not applicable as a distance in traditional fuzzy C-means (FCM) algorithms, and information of a distance change in a distance axis can only be known, a lack of smoothness of a shape change cannot be solved because the distance lacks information of time, and a good correct rate of category clustering cannot be provided because a shape variation is decided by a slope. Traditional fuzzy slope time series (FSTS) algorithms are not adaptable for unstable wave motions and instant change in angle, information of a relative change of a similar shape trend in a time axis can only be known, rapid fluctuations in a long time sequence of an economic time or a cycle swing of a trend curve cannot be solved because of an insufficient information of the trend curve, and correct rate of category clustering cannot be provided because a wave motion variation is decided by an angle. Traditional fuzzy spectral angle matching (F SAM) algorithms are not adaptable for length change of the distance, a relative polarity of three axis between variables and an origin, and a change in a relationship between a stability and a wave motion angle can only be known, a distance variation of fast moving, long distance, short distance in distance cannot be provided because of an insufficient information of the distance caused by drastic changes of the trend curve, and correct rate of category clustering cannot be provided because variation of the distance is decided by the distance.
Traditional data transmitting and receiving are carried out directly by transmission lines, rarely by channel modules. Even if the channel modules are used, only hardware structure processing is involved. They have nothing to do with analyzing category clustering data, and a processing method for analyzing category clustering data and combine an ideal combination channel of variables cannot be found.